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Skeleton Based Action Recognition

Skeleton-based Action Recognition is a computer vision task that involves recognizing human actions from a sequence of 3D skeletal joint data captured from sensors such as Microsoft Kinect, Intel RealSense, and wearable devices. The goal of skeleton-based action recognition is to develop algorithms that can understand and classify human actions from skeleton data, which can be used in various applications such as human-computer interaction, sports analysis, and surveillance.

( Image credit: View Adaptive Neural Networks for High Performance Skeleton-based Human Action Recognition )

Papers

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Showing 376–400 of 419 papers

TitleStatusHype
Motion Feature Augmented Recurrent Neural Network for Skeleton-based Dynamic Hand Gesture Recognition—0
Enhanced skeleton visualization for view invariant human action recognition—0
Skeleton-Based Human Action Recognition with Global Context-Aware Attention LSTM Networks—0
Skeleton-based Action Recognition Using LSTM and CNN—0
Learning Human Pose Models from Synthesized Data for Robust RGB-D Action Recognition—0
Spatio-Temporal Naive-Bayes Nearest-Neighbor (ST-NBNN) for Skeleton-Based Action RecognitionCode0
Global Context-Aware Attention LSTM Networks for 3D Action Recognition—0
Skeleton-Based Action Recognition Using Spatio-Temporal LSTM Network with Trust Gates—0
Two-Stream 3D Convolutional Neural Network for Skeleton-Based Action Recognition—0
Investigation of Different Skeleton Features for CNN-based 3D Action RecognitionCode0
Skeleton based action recognition using translation-scale invariant image mapping and multi-scale deep cnn—0
Interpretable 3D Human Action Analysis with Temporal Convolutional NetworksCode0
Modeling Temporal Dynamics and Spatial Configurations of Actions Using Two-Stream Recurrent Neural Networks—0
Chained Multi-stream Networks Exploiting Pose, Motion, and Appearance for Action Classification and DetectionCode0
Skeletonnet: Mining deep part features for 3-d action recognition—0
View Adaptive Recurrent Neural Networks for High Performance Human Action Recognition from Skeleton DataCode0
A New Representation of Skeleton Sequences for 3D Action Recognition—0
On Geometric Features for Skeleton-Based Action Recognition using Multilayer LSTM NetworksCode0
Learning Linear Dynamical Systems with High-Order Tensor Data for Skeleton based Action Recognition—0
Action Recognition Based on Joint Trajectory Maps with Convolutional Neural Networks—0
Deep Learning on Lie Groups for Skeleton-based Action Recognition—0
Jointly learning heterogeneous features for rgb-d activity recognition—0
FlowNet 2.0: Evolution of Optical Flow Estimation with Deep NetworksCode0
An End-to-End Spatio-Temporal Attention Model for Human Action Recognition from Skeleton Data—0
Multi-region two-stream R-CNN for action detection—0
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